No abstract

Quality sleep is an essential part of health and well-being. Yet fractured sleep is disturbingly prevalent in our society, partly due to insults from a variety of noises [1]. Common experience suggests that this fragility of sleep is highly variable between people, but it is unclear what mechanisms drive these differences. Here we show that it is possible to predict an individual's ability to maintain sleep in the face of sound using spontaneous brain rhythms from electroencephalography (EEG). The sleep spindle is a thalamocortical rhythm manifested on the EEG as a brief 11-15 Hz oscillation and is thought to be capable of modulating the influence of external stimuli [2]. Its rate of occurrence, while variable across people, is stable across nights [3]. We found that individuals who generated more sleep spindles during a quiet night of sleep went on to exhibit higher tolerance for noise during a subsequent, noisy night of sleep. This result shows that the sleeping brain's spontaneous activity heralds individual resilience to disruptive stimuli. Our finding sets the stage for future studies that attempt to augment spindle production to enhance sleep continuity when confronted with noise.

Background: Deep learning has the potential to augment the use of chest radiography in clinical radiology, but challenges include poor generalizability, spectrum bias, and difficulty comparing across studies.Purpose: To develop and evaluate deep learning models for chest radiograph interpretation by using radiologist-adjudicated reference standards. Materials and Methods:Deep learning models were developed to detect four findings (pneumothorax, opacity, nodule or mass, and fracture) on frontal chest radiographs. This retrospective study used two data sets. Data set 1 (DS1) consisted of 759 611 images from a multicity hospital network and ChestX-ray14 is a publicly available data set with 112 120 images. Natural language processing and expert review of a subset of images provided labels for 657 954 training images. Test sets consisted of 1818 and 1962 images from DS1 and ChestX-ray14, respectively. Reference standards were defined by radiologist-adjudicated image review. Performance was evaluated by area under the receiver operating characteristic curve analysis, sensitivity, specificity, and positive predictive value. Four radiologists reviewed test set images for performance comparison. Inverse probability weighting was applied to DS1 to account for positive radiograph enrichment and estimate population-level performance.

SUMMARYThe diagnosis and management of insomnia relies primarily on clinical history. However, patient self-report of sleep-wake times may not agree with objective measurements. We hypothesized that those with shallow or fragmented sleep would under-report sleep quantity, and that this might account for some of the mismatch. We compared objective and subjective sleep-wake times for 277 patients who underwent diagnostic polysomnography. The group included those with insomnia symptoms (n = 92), obstructive sleep apnea (n = 66) or both (n = 119). Mismatch of wake duration was context dependent: all three groups overestimated sleep latency but underestimated wakefulness after sleep onset. The insomnia group underestimated total sleep time by a median of 81 min. However, contrary to our hypothesis, measures of fragmentation (N1, arousal index, sleep efficiency, etc.) did not correlate with the subjective sleep duration estimates. To unmask a potential relationship between sleep architecture and subjective duration, we tested three hypotheses: N1 is perceived as wake; sleep bouts under 10 min are perceived as wake; or N1 and N2 are perceived in a weighted fashion. None of these hypotheses exposed a match between subjective and objective sleep duration. We show only modest performance of a Naïve Bayes Classifier algorithm for predicting mismatch using clinical and polysomnographic variables. Subjectiveobjective mismatch is common in patients reporting insomnia symptoms. We conclude that mismatch was not attributable to commonly measured polysomnographic measures of fragmentation. Further insight is needed into the complex relationships between subjective perception of sleep and conventional, objective measurements.

Academy of Architecture for Health, Facilities Guidelines Institute, and The Center for Health Design.